Drug Gene-Set Analysis Identifies Established and Emerging Therapeutic Mechanisms

Developing novel disease therapies is challenging, especially for central nervous system (CNS) disorders, which have a clinical approval rate of 6.2%. Genetic evidence is associated with higher success rates in drug development, but systematically translating genome-wide association study (GWAS) signals into actionable therapeutic hypotheses remains difficult. Here, we present DRUGSETS, a framework that tests whether drug (n = 8,136) and mechanism-of-action (MoA; n = 384) gene sets are enriched for GWAS associations. We applied DRUGSETS to five CNS and five non-CNS phenotypes using competitive gene-set analysis with both the standard MAGMA gene-level statistic and Polygenic Priority Scores (PoPS). Both implementations were enriched for approved treatments, and drug gene set significance was associated with further progression in clinical trials. Across all ten phenotypes, we identified 69 MoA and 589 drug gene set-associations that were study-wide significant, finding both established and novel pharmacological targets. Non-CNS traits were enriched for immune, hemostatic and metabolic mechanisms. Psychiatric disorders converged on mechanisms regulating neuronal excitation/inhibition, and Alzheimers disease was enriched for immune-related mechanisms and amyloid aggregation. DRUGSETS identifies shared and disease-specific therapeutic biology, including mechanisms not targeted by current treatments. DRUGSETS is available as an open-source tool and is being integrated into FUMA.